[TOC]
Every programming language has tools for effectively handling the duplication of concepts. In Rust, one such tool is generics: abstract stand-ins for concrete types or other properties. We can express the behavior of generics or how they relate to other generics without knowing what will be in their place when compiling and running the code.
Functions can take parameters of some generic type, instead of a concrete type
like i32 or String, in the same way a function takes parameters with
unknown values to run the same code on multiple concrete values. In fact, we’ve
already used generics in Chapter 6 with Option<T>, Chapter 8 with Vec<T>
and HashMap<K, V>, and Chapter 9 with Result<T, E>. In this chapter, you’ll
explore how to define your own types, functions, and methods with generics!
First, we’ll review how to extract a function to reduce code duplication. We’ll then use the same technique to make a generic function from two functions that differ only in the types of their parameters. We’ll also explain how to use generic types in struct and enum definitions.
Then you’ll learn how to use traits to define behavior in a generic way. You can combine traits with generic types to constrain a generic type to accept only those types that have a particular behavior, as opposed to just any type.
Finally, we’ll discuss lifetimes: a variety of generics that give the compiler information about how references relate to each other. Lifetimes allow us to give the compiler enough information about borrowed values so that it can ensure references will be valid in more situations than it could without our help.
Generics allow us to replace specific types with a placeholder that represents multiple types to remove code duplication.
Before diving into generics syntax, then, let’s first look at how to remove duplication in a way that doesn’t involve generic types by extracting a function that replaces specific values with a placeholder that represents multiple values. Then we’ll apply the same technique to extract a generic function! By looking at how to recognize duplicated code you can extract into a function, you’ll start to recognize duplicated code that can use generics.
We begin with the short program in Listing 10-1 that finds the largest number in a list.
Filename: src/main.rs
fn main() {
let number_list = vec![34, 50, 25, 100, 65];
let mut largest = number_list[0];
for number in number_list {
if number > largest {
largest = number;
}
}
println!("The largest number is {}", largest);
}
Listing 10-1: Finding the largest number in a list of numbers
We store a list of integers in the variable number_list and place the first
number in the list in a variable named largest. We then iterate through all
the numbers in the list, and if the current number is greater than the number
stored in largest, replace the number in that variable. However, if the
current number is less than or equal to the largest number seen so far, the
variable doesn’t change, and the code moves on to the next number in the list.
After considering all the numbers in the list, largest should hold the
largest number, which in this case is 100.
We've now been tasked with finding the largest number in two different lists of numbers. To do so, we can choose to duplicate the code in Listing 10-1 and use the same logic at two different places in the program, as shown in Listing 10-2.
Filename: src/main.rs
fn main() {
let number_list = vec![34, 50, 25, 100, 65];
let mut largest = number_list[0];
for number in number_list {
if number > largest {
largest = number;
}
}
println!("The largest number is {}", largest);
let number_list = vec![102, 34, 6000, 89, 54, 2, 43, 8];
let mut largest = number_list[0];
for number in number_list {
if number > largest {
largest = number;
}
}
println!("The largest number is {}", largest);
}
Listing 10-2: Code to find the largest number in two lists of numbers
Although this code works, duplicating code is tedious and error prone. We also have to remember to update the code in multiple places when we want to change it.
To eliminate this duplication, we’ll create an abstraction by defining a function that operates on any list of integers passed in a parameter. This solution makes our code clearer and lets us express the concept of finding the largest number in a list abstractly.
In Listing 10-3, we extract the code that finds the largest number into a
function named largest. Then we call the function to find the largest number
in the two lists from Listing 10-2. We could also use the function on any other
list of i32 values we might have in the future.
Filename: src/main.rs
fn largest(list: &[i32]) -> i32 {
let mut largest = list[0];
for &item in list {
if item > largest {
largest = item;
}
}
largest
}
fn main() {
let number_list = vec![34, 50, 25, 100, 65];
let result = largest(&number_list);
println!("The largest number is {}", result);
let number_list = vec![102, 34, 6000, 89, 54, 2, 43, 8];
let result = largest(&number_list);
println!("The largest number is {}", result);
}
Listing 10-3: Abstracted code to find the largest number in two lists
The largest function has a parameter called list, which represents any
concrete slice of i32 values we might pass into the function. As a result,
when we call the function, the code runs on the specific values that we pass
in. Don’t worry about the syntax of the for loop for now. We aren’t
referencing a reference to an i32 here; we’re pattern matching and
destructuring each &i32 that the for loop gets so that item will be an
i32 inside the loop body. We’ll cover pattern matching in detail in Chapter
18.
In sum, here are the steps we took to change the code from Listing 10-2 to Listing 10-3:
- Identify duplicate code.
- Extract the duplicate code into the body of the function and specify the inputs and return values of that code in the function signature.
- Update the two instances of duplicated code to call the function instead.
Next, we’ll use these same steps with generics to reduce code duplication. In
the same way that the function body can operate on an abstract list instead
of specific values, generics allow code to operate on abstract types.
For example, say we had two functions: one that finds the largest item in a
slice of i32 values and one that finds the largest item in a slice of char
values. How would we eliminate that duplication? Let’s find out!
We use generics to create definitions for items like function signatures or structs, which we can then use with many different concrete data types. Let’s first look at how to define functions, structs, enums, and methods using generics. Then we’ll discuss how generics affect code performance.
When defining a function that uses generics, we place the generics in the signature of the function where we would usually specify the data types of the parameters and return value. Doing so makes our code more flexible and provides more functionality to callers of our function while preventing code duplication.
Continuing with our largest function, Listing 10-4 shows two functions that
both find the largest value in a slice. We'll then combine these into a single
function that uses generics.
Filename: src/main.rs
fn largest_i32(list: &[i32]) -> i32 {
let mut largest = list[0];
for &item in list {
if item > largest {
largest = item;
}
}
largest
}
fn largest_char(list: &[char]) -> char {
let mut largest = list[0];
for &item in list {
if item > largest {
largest = item;
}
}
largest
}
fn main() {
let number_list = vec![34, 50, 25, 100, 65];
let result = largest_i32(&number_list);
println!("The largest number is {}", result);
let char_list = vec!['y', 'm', 'a', 'q'];
let result = largest_char(&char_list);
println!("The largest char is {}", result);
}
Listing 10-4: Two functions that differ only in their names and the types in their signatures
The largest_i32 function is the one we extracted in Listing 10-3 that finds
the largest i32 in a slice. The largest_char function finds the largest
char in a slice. The function bodies have the same code, so let’s eliminate
the duplication by introducing a generic type parameter in a single function.
To parameterize the types in a new single function, we need to name the type
parameter, just as we do for the value parameters to a function. You can use
any identifier as a type parameter name. But we’ll use T because, by
convention, parameter names in Rust are short, often just a letter, and Rust’s
type-naming convention is CamelCase. Short for “type,” T is the default
choice of most Rust programmers.
When we use a parameter in the body of the function, we have to declare the
parameter name in the signature so the compiler knows what that name means.
Similarly, when we use a type parameter name in a function signature, we have
to declare the type parameter name before we use it. To define the generic
largest function, place type name declarations inside angle brackets, <>,
between the name of the function and the parameter list, like this:
fn largest<T>(list: &[T]) -> T {
We read this definition as: the function largest is generic over some type
T. This function has one parameter named list, which is a slice of values
of type T. The largest function will return a value of the
same type T.
Listing 10-5 shows the combined largest function definition using the generic
data type in its signature. The listing also shows how we can call the function
with either a slice of i32 values or char values. Note that this code won’t
compile yet, but we’ll fix it later in this chapter.
Filename: src/main.rs
fn largest<T>(list: &[T]) -> T {
let mut largest = list[0];
for &item in list {
if item > largest {
largest = item;
}
}
largest
}
fn main() {
let number_list = vec![34, 50, 25, 100, 65];
let result = largest(&number_list);
println!("The largest number is {}", result);
let char_list = vec!['y', 'm', 'a', 'q'];
let result = largest(&char_list);
println!("The largest char is {}", result);
}
Listing 10-5: The largest function using generic type parameters; this
doesn’t yet compile yet
If we compile this code right now, we’ll get this error:
error[E0369]: binary operation `>` cannot be applied to type `T`
--> src/main.rs:5:17
|
5 | if item > largest {
| ---- ^ ------- T
| |
| T
|
help: consider restricting type parameter `T`
|
1 | fn largest<T: std::cmp::PartialOrd>(list: &[T]) -> T {
| ^^^^^^^^^^^^^^^^^^^^^^
The note mentions std::cmp::PartialOrd, which is a trait. We’ll talk about
traits in the next section. For now, know that this error states that the body
of largest won’t work for all possible types that T could be. Because we
want to compare values of type T in the body, we can only use types whose
values can be ordered. To enable comparisons, the standard library has the
std::cmp::PartialOrd trait that you can implement on types (see Appendix C
for more on this trait). You’ll learn how to specify that a generic type has a
particular trait in the “Traits as Parameters” section. Before we fix this code
(in the section “Fixing the largest Function with Trait Bounds”), let’s first
explore other ways of using generic type parameters.
We can also define structs to use a generic type parameter in one or more
fields using the <> syntax. Listing 10-6 defines a Point<T> struct to hold
x and y coordinate values of any type.
Filename: src/main.rs
struct Point<T> {
x: T,
y: T,
}
fn main() {
let integer = Point { x: 5, y: 10 };
let float = Point { x: 1.0, y: 4.0 };
}
Listing 10-6: A Point<T> struct that holds x and y values of type T
The syntax for using generics in struct definitions is similar to that used in function definitions. First, we declare the name of the type parameter inside angle brackets just after the name of the struct. Then we use the generic type in the struct definition where we would otherwise specify concrete data types.
Note that because we’ve used only one generic type to define Point<T>, this
definition says that the Point<T> struct is generic over some type T, and
the fields x and y are both that same type, whatever that type may be. If
we create an instance of a Point<T> that has values of different types, as in
Listing 10-7, our code won’t compile.
Filename: src/main.rs
struct Point<T> {
x: T,
y: T,
}
fn main() {
let wont_work = Point { x: 5, y: 4.0 };
}
Listing 10-7: The fields x and y must be the same type because both have
the same generic data type T.
In this example, when we assign the integer value 5 to x, we let the compiler
know that the generic type T will be an integer for this instance of
Point<T>. Then when we specify 4.0 for y, which we’ve defined to have the
same type as x, we’ll get a type mismatch error like this:
error[E0308]: mismatched types
--> src/main.rs:7:38
|
7 | let wont_work = Point { x: 5, y: 4.0 };
| ^^^ expected integer, found floating-point number
To define a Point struct where x and y are both generics but could have
different types, we can use multiple generic type parameters. For example, in
Listing 10-8, we change the definition of Point to be generic over types T
and U where x is of type T and y is of type U.
Filename: src/main.rs
struct Point<T, U> {
x: T,
y: U,
}
fn main() {
let both_integer = Point { x: 5, y: 10 };
let both_float = Point { x: 1.0, y: 4.0 };
let integer_and_float = Point { x: 5, y: 4.0 };
}
Listing 10-8: A Point<T, U> generic over two types so that x and y can be
values of different types
Now all the instances of Point shown are allowed! You can use as many generic
type parameters in a definition as you want, but using more than a few makes
your code hard to read. If you're finding you need lots of generic types in
your code, it could indicate that your code needs restructuring into smaller
pieces.
As we did with structs, we can define enums to hold generic data types in their
variants. Let’s take another look at the Option<T> enum that the standard
library provides, which we used in Chapter 6:
enum Option<T> {
Some(T),
None,
}
This definition should now make more sense to you. As you can see, the
Option<T> enum is generic over type T and has two variants: Some, which
holds one value of type T, and a None variant that doesn’t hold any value.
By using the Option<T> enum, we can express the abstract concept of an
optional value, and because Option<T> is generic, we can use this abstraction
no matter what the type of the optional value is.
Enums can use multiple generic types as well. The definition of the Result
enum that we used in Chapter 9 is one example:
enum Result<T, E> {
Ok(T),
Err(E),
}
The Result enum is generic over two types, T and E, and has two variants:
Ok, which holds a value of type T, and Err, which holds a value of type
E. This definition makes it convenient to use the Result enum anywhere we
have an operation that might succeed (return a value of some type T) or fail
(return an error of some type E). In fact, this is what we used to open a
file in Listing 9-3, where T was filled in with the type std::fs::File when
the file was opened successfully and E was filled in with the type
std::io::Error when there were problems opening the file.
When you recognize situations in your code with multiple struct or enum definitions that differ only in the types of the values they hold, you can avoid duplication by using generic types instead.
We can implement methods on structs and enums (as we did in Chapter 5) and use
generic types in their definitions, too. Listing 10-9 shows the Point<T>
struct we defined in Listing 10-6 with a method named x implemented on it.
Filename: src/main.rs
struct Point<T> {
x: T,
y: T,
}
impl<T> Point<T> {
fn x(&self) -> &T {
&self.x
}
}
fn main() {
let p = Point { x: 5, y: 10 };
println!("p.x = {}", p.x());
}
Listing 10-9: Implementing a method named x on the Point<T> struct that
will return a reference to the x field of type T
Here, we’ve defined a method named x on Point<T> that returns a reference
to the data in the field x.
Note that we have to declare T just after impl so we can use T to specify
that we’re implementing methods on the type Point<T>. By declaring T as a
generic type after impl, Rust can identify that the type in the angle
brackets in Point is a generic type rather than a concrete type. We could
have chosen a different name for this generic parameter than the generic
parameter declared in the struct definition, but using the same name is
conventional. Methods written within an impl that declares the generic type
will be defined on any instance of the type, no matter what concrete type ends
up substituting for the generic type.
We can also specify constraints on generic types when defining methods on the
type. We could, for example, implement methods only on Point<f32> instances
rather than on Point<T> instances with any generic type. In Listing 10-10 we
use the concrete type f32, meaning we don’t declare any types after impl.
Filename: src/main.rs
impl Point<f32> {
fn distance_from_origin(&self) -> f32 {
(self.x.powi(2) + self.y.powi(2)).sqrt()
}
}
Listing 10-10: An impl block that only applies to a struct with a particular
concrete type for the generic type parameter T
This code means the type Point<f32> will have a distance_from_origin
method; other instances of Point<T> where T is not of type f32 will not
have this method defined. The method measures how far our point is from the
point at coordinates (0.0, 0.0) and uses mathematical operations that are
available only for floating point types.
Generic type parameters in a struct definition aren’t always the same as those
you use in that same struct’s method signatures. Listing 10-11 uses the generic
types X1 and Y1 for the Point struct and X2 Y2 for the mixup method
signature to make the example clearer. The method creates a new Point
instance with the x value from the self Point (of type X1) and the y
value from the passed-in Point (of type Y2).
Filename: src/main.rs
struct Point<X1, Y1> {
x: X1,
y: Y1,
}
impl<X1, Y1> Point<X1, Y1> {
fn mixup<X2, Y2>(self, other: Point<X2, Y2>) -> Point<X1, Y2> {
Point {
x: self.x,
y: other.y,
}
}
}
fn main() {
let p1 = Point { x: 5, y: 10.4 };
let p2 = Point { x: "Hello", y: 'c' };
let p3 = p1.mixup(p2);
println!("p3.x = {}, p3.y = {}", p3.x, p3.y);
}
Listing 10-11: A method that uses generic types different from its struct’s definition
In main, we’ve defined a Point that has an i32 for x (with value 5)
and an f64 for y (with value 10.4). The p2 variable is a Point struct
that has a string slice for x (with value "Hello") and a char for y
(with value c). Calling mixup on p1 with the argument p2 gives us p3,
which will have an i32 for x, because x came from p1. The p3 variable
will have a char for y, because y came from p2. The println! macro
call will print p3.x = 5, p3.y = c.
The purpose of this example is to demonstrate a situation in which some generic
parameters are declared with impl and some are declared with the method
definition. Here, the generic parameters X1 and Y1 are declared after
impl because they go with the struct definition. The generic parameters X2
and Y2 are declared after fn mixup, because they’re only relevant to the
method.
You might be wondering whether there is a runtime cost when using generic type parameters. The good news is that using generic types won't make your run any slower than it would with concrete types.
Rust accomplishes this by performing monomorphization of the code using generics at compile time. Monomorphization is the process of turning generic code into specific code by filling in the concrete types that are used when compiled. In this process, the compiler does the opposite of the steps we used to create the generic function in Listing 10-5: the compiler looks at all the places where generic code is called and generates code for the concrete types the generic code is called with.
Let’s look at how this works by using the standard library’s generic
Option<T> enum:
let integer = Some(5);
let float = Some(5.0);
When Rust compiles this code, it performs monomorphization. During that
process, the compiler reads the values that have been used in Option<T>
instances and identifies two kinds of Option<T>: one is i32 and the other
is f64. As such, it expands the generic definition of Option<T> into
Option_i32 and Option_f64, thereby replacing the generic definition with
the specific ones.
The monomorphized version of the code looks like the following:
Filename: src/main.rs
enum Option_i32 {
Some(i32),
None,
}
enum Option_f64 {
Some(f64),
None,
}
fn main() {
let integer = Option_i32::Some(5);
let float = Option_f64::Some(5.0);
}
The generic Option<T> is replaced with the specific definitions created by
the compiler. Because Rust compiles generic code into code that specifies the
type in each instance, we pay no runtime cost for using generics. When the code
runs, it performs just as it would if we had duplicated each definition by
hand. The process of monomorphization makes Rust’s generics extremely efficient
at runtime.
A trait defines functionality a particular type has and can share with other types. We can use traits to define shared behavior in an abstract way. We can use trait bounds to specify that a generic type can be any type that has certain behavior.
Note: Traits are similar to a feature often called interfaces in other languages, although with some differences.
A type’s behavior consists of the methods we can call on that type. Different types share the same behavior if we can call the same methods on all of those types. Trait definitions are a way to group method signatures together to define a set of behaviors necessary to accomplish some purpose.
For example, let’s say we have multiple structs that hold various kinds and
amounts of text: a NewsArticle struct that holds a news story filed in a
particular location and a Tweet that can have at most 280 characters along
with metadata that indicates whether it was a new tweet, a retweet, or a reply
to another tweet.
We want to make a media aggregator library crate named aggregator that can
display summaries of data that might be stored in a NewsArticle or Tweet
instance. To do this, we need a summary from each type, and we’ll request
that summary by calling a summarize method on an instance. Listing 10-12
shows the definition of a public Summary trait that expresses this behavior.
Filename: src/lib.rs
pub trait Summary {
fn summarize(&self) -> String;
}
Listing 10-12: A Summary trait that consists of the behavior provided by a
summarize method
Here, we declare a trait using the trait keyword and then the trait’s name,
which is Summary in this case. We’ve also declared the trait as pub so that
crates depending on this crate can make use of this trait too, as we’ll see in
a few examples. Inside the curly brackets, we declare the method signatures
that describe the behaviors of the types that implement this trait, which in
this case is fn summarize(&self) -> String.
After the method signature, instead of providing an implementation within curly
brackets, we use a semicolon. Each type implementing this trait must provide
its own custom behavior for the body of the method. The compiler will enforce
that any type that has the Summary trait will have the method summarize
defined with this signature exactly.
A trait can have multiple methods in its body: the method signatures are listed one per line and each line ends in a semicolon.
Now that we’ve defined the desired signatures of the Summary trait’s methods,
we can implement it on the types in our media aggregator. Listing 10-13 shows
an implementation of the Summary trait on the NewsArticle struct that uses
the headline, the author, and the location to create the return value of
summarize. For the Tweet struct, we define summarize as the username
followed by the entire text of the tweet, assuming that tweet content is
already limited to 280 characters.
Filename: src/lib.rs
pub struct NewsArticle {
pub headline: String,
pub location: String,
pub author: String,
pub content: String,
}
impl Summary for NewsArticle {
fn summarize(&self) -> String {
format!("{}, by {} ({})", self.headline, self.author, self.location)
}
}
pub struct Tweet {
pub username: String,
pub content: String,
pub reply: bool,
pub retweet: bool,
}
impl Summary for Tweet {
fn summarize(&self) -> String {
format!("{}: {}", self.username, self.content)
}
}
Listing 10-13: Implementing the Summary trait on the NewsArticle and
Tweet types
Implementing a trait on a type is similar to implementing regular methods. The
difference is that after impl, we put the trait name we want to implement,
then use the for keyword, and then specify the name of the type we want to
implement the trait for. Within the impl block, we put the method signatures
that the trait definition has defined. Instead of adding a semicolon after each
signature, we use curly brackets and fill in the method body with the specific
behavior that we want the methods of the trait to have for the particular type.
Now that the library has implemented the Summary trait on NewsArticle and
Tweet, users of the crate can call the trait methods on instances of
NewsArticle and Tweet in the same way we call regular methods. The only
difference is that the user must bring the trait into scope as well as the
types. Here’s an example of how a binary crate could use our aggregator
library crate:
use aggregator::{Summary, Tweet};
fn main() {
let tweet = Tweet {
username: String::from("horse_ebooks"),
content: String::from(
"of course, as you probably already know, people",
),
reply: false,
retweet: false,
};
println!("1 new tweet: {}", tweet.summarize());
}
This code prints 1 new tweet: horse_ebooks: of course, as you probably already know, people.
Other crates that depend on the aggregator crate can also bring the Summary
trait into scope to implement Summary on their own types. One restriction to
note is that we can implement a trait on a type only if at least one of the
trait or the type is local to our crate. For example, we can implement standard
library traits like Display on a custom type like Tweet as part of our
aggregator crate functionality, because the type Tweet is local to our
aggregator crate. We can also implement Summary on Vec<T> in our
aggregator crate, because the trait Summary is local to our aggregator
crate.
But we can’t implement external traits on external types. For example, we can’t
implement the Display trait on Vec<T> within our aggregator crate,
because Display and Vec<T> are both defined in the standard library and
aren’t local to our aggregator crate. This restriction is part of a property
called coherence, and more specifically the orphan rule, so named because
the parent type is not present. This rule ensures that other people’s code
can’t break your code and vice versa. Without the rule, two crates could
implement the same trait for the same type, and Rust wouldn’t know which
implementation to use.
Sometimes it’s useful to have default behavior for some or all of the methods in a trait instead of requiring implementations for all methods on every type. Then, as we implement the trait on a particular type, we can keep or override each method’s default behavior.
In Listing 10-14 we specify a default string for the summarize method of the
Summary trait instead of only defining the method signature, as we did in
Listing 10-12.
Filename: src/lib.rs
pub trait Summary {
fn summarize(&self) -> String {
String::from("(Read more...)")
}
}
Listing 10-14: Defining a Summary trait with a default implementation of
the summarize method
To use a default implementation to summarize instances of NewsArticle, we
specify an empty impl block with impl Summary for NewsArticle {}.
Even though we’re no longer defining the summarize method on NewsArticle
directly, we’ve provided a default implementation and specified that
NewsArticle implements the Summary trait. As a result, we can still call
the summarize method on an instance of NewsArticle, like this:
let article = NewsArticle {
headline: String::from("Penguins win the Stanley Cup Championship!"),
location: String::from("Pittsburgh, PA, USA"),
author: String::from("Iceburgh"),
content: String::from(
"The Pittsburgh Penguins once again are the best \
hockey team in the NHL.",
),
};
println!("New article available! {}", article.summarize());
This code prints New article available! (Read more...).
Creating a default implementation doesn’t require us to change anything about
the implementation of Summary on Tweet in Listing 10-13. The reason is that
the syntax for overriding a default implementation is the same as the syntax
for implementing a trait method that doesn’t have a default implementation.
Default implementations can call other methods in the same trait, even if those
other methods don’t have a default implementation. In this way, a trait can
provide a lot of useful functionality and only require implementors to specify
a small part of it. For example, we could define the Summary trait to have a
summarize_author method whose implementation is required, and then define a
summarize method that has a default implementation that calls the
summarize_author method:
pub trait Summary {
fn summarize_author(&self) -> String;
fn summarize(&self) -> String {
format!("(Read more from {}...)", self.summarize_author())
}
}
To use this version of Summary, we only need to define summarize_author
when we implement the trait on a type:
impl Summary for Tweet {
fn summarize_author(&self) -> String {
format!("@{}", self.username)
}
}
After we define summarize_author, we can call summarize on instances of the
Tweet struct, and the default implementation of summarize will call the
definition of summarize_author that we’ve provided. Because we’ve implemented
summarize_author, the Summary trait has given us the behavior of the
summarize method without requiring us to write any more code.
let tweet = Tweet {
username: String::from("horse_ebooks"),
content: String::from(
"of course, as you probably already know, people",
),
reply: false,
retweet: false,
};
println!("1 new tweet: {}", tweet.summarize());
This code prints 1 new tweet: (Read more from @horse_ebooks...).
Note that it isn’t possible to call the default implementation from an overriding implementation of that same method.
Now that you know how to define and implement traits, we can explore how to use
traits to define functions that accept many different types. We'll use the
Summary trait we implemented on the NewsArticle and Tweet types in
Listing 10-13 to define a notify function that calls the summarize method
on its item parameter, which is of some type that implements the Summary
trait. To do this, we use the impl Trait syntax, like this:
pub fn notify(item: &impl Summary) {
println!("Breaking news! {}", item.summarize());
}
Instead of a concrete type for the item parameter, we specify the impl
keyword and the trait name. This parameter accepts any type that implements the
specified trait. In the body of notify, we can call any methods on item
that come from the Summary trait, such as summarize. We can call notify
and pass in any instance of NewsArticle or Tweet. Code that calls the
function with any other type, such as a String or an i32, won’t compile
because those types don’t implement Summary.
The impl Trait syntax works for straightforward cases but is actually syntax
sugar for a longer form known as a trait bound; it looks like this:
pub fn notify<T: Summary>(item: &T) {
println!("Breaking news! {}", item.summarize());
}
This longer form is equivalent to the example in the previous section but is more verbose. We place trait bounds with the declaration of the generic type parameter after a colon and inside angle brackets.
The impl Trait syntax is convenient and makes for more concise code in simple
cases, while the fuller trait bound syntax can express more complexity in other
cases.
For example, we can have two parameters that implement Summary. Doing so with
the impl Trait syntax looks like this:
pub fn notify(item1: &impl Summary, item2: &impl Summary) {
Using impl Trait is appropriate if we want this function to allow item1 and
item2 to have different types (as long as both types implement Summary). If
we want to force both parameters to have the same type, however, we must use a
trait bound, like this:
pub fn notify<T: Summary>(item1: &T, item2: &T) {
The generic type T specified as the type of the item1 and item2
parameters constrains the function such that the concrete type of the value
passed as an argument for item1 and item2 must be the same.
We can also specify more than one trait bound. Say we wanted notify to use
display formatting as well as summarize on item: we specify in the notify
definition that item must implement both Display and Summary. We can do
so using the + syntax:
pub fn notify(item: &(impl Summary + Display)) {
The + syntax is also valid with trait bounds on generic types:
pub fn notify<T: Summary + Display>(item: &T) {
With the two trait bounds specified, the body of notify can call summarize
and use {} to format item.
Using too many trait bounds has its downsides. Each generic has its own trait
bounds, so functions with multiple generic type parameters can contain lots of
trait bound information between the function’s name and its parameter list,
making the function signature hard to read. For this reason, Rust has alternate
syntax for specifying trait bounds inside a where clause after the function
signature. So instead of writing this:
fn some_function<T: Display + Clone, U: Clone + Debug>(t: &T, u: &U) -> i32 {
we can use a where clause, like this:
fn some_function<T, U>(t: &T, u: &U) -> i32
where T: Display + Clone,
U: Clone + Debug
{
This function’s signature is less cluttered: the function name, parameter list, and return type are close together, similar to a function without lots of trait bounds.
We can also use the impl Trait syntax in the return position to return a
value of some type that implements a trait, as shown here:
fn returns_summarizable() -> impl Summary {
Tweet {
username: String::from("horse_ebooks"),
content: String::from(
"of course, as you probably already know, people",
),
reply: false,
retweet: false,
}
}
By using impl Summary for the return type, we specify that the
returns_summarizable function returns some type that implements the Summary
trait without naming the concrete type. In this case, returns_summarizable
returns a Tweet, but the code calling this function doesn’t need to know that.
The ability to specify a return type only by the trait it implements is
especially useful in the context of closures and iterators, which we cover in
Chapter 13. Closures and iterators create types that only the compiler knows or
types that are very long to specify. The impl Trait syntax lets you concisely
specify that a function returns some type that implements the Iterator trait
without needing to write out a very long type.
However, you can only use impl Trait if you’re returning a single type. For
example, this code that returns either a NewsArticle or a Tweet with the
return type specified as impl Summary wouldn’t work:
fn returns_summarizable(switch: bool) -> impl Summary {
if switch {
NewsArticle {
headline: String::from(
"Penguins win the Stanley Cup Championship!",
),
location: String::from("Pittsburgh, PA, USA"),
author: String::from("Iceburgh"),
content: String::from(
"The Pittsburgh Penguins once again are the best \
hockey team in the NHL.",
),
}
} else {
Tweet {
username: String::from("horse_ebooks"),
content: String::from(
"of course, as you probably already know, people",
),
reply: false,
retweet: false,
}
}
}
Returning either a NewsArticle or a Tweet isn’t allowed due to restrictions
around how the impl Trait syntax is implemented in the compiler. We’ll cover
how to write a function with this behavior in the “Using Trait Objects That
Allow for Values of Different
Types” section of Chapter 17.
Now that you know how to specify the behavior you want using the generic type
parameter’s bounds, let’s return to Listing 10-5 to fix the definition of the
largest function that uses a generic type parameter! Last time we tried to
run that code, we received this error:
error[E0369]: binary operation `>` cannot be applied to type `T`
--> src/main.rs:5:17
|
5 | if item > largest {
| ---- ^ ------- T
| |
| T
|
help: consider restricting type parameter `T`
|
1 | fn largest<T: std::cmp::PartialOrd>(list: &[T]) -> T {
| ^^^^^^^^^^^^^^^^^^^^^^
In the body of largest we wanted to compare two values of type T using the
greater than (>) operator. Because that operator is defined as a default
method on the standard library trait std::cmp::PartialOrd, we need to specify
PartialOrd in the trait bounds for T so the largest function can work on
slices of any type that we can compare. We don’t need to bring PartialOrd
into scope because it’s in the prelude. Change the signature of largest to
look like this:
fn largest<T: PartialOrd>(list: &[T]) -> T {
This time when we compile the code, we get a different set of errors:
error[E0508]: cannot move out of type `[T]`, a non-copy slice
--> src/main.rs:2:23
|
2 | let mut largest = list[0];
| ^^^^^^^
| |
| cannot move out of here
| move occurs because `list[_]` has type `T`, which does not implement the `Copy` trait
| help: consider borrowing here: `&list[0]`
error[E0507]: cannot move out of a shared reference
--> src/main.rs:4:18
|
4 | for &item in list {
| ----- ^^^^
| ||
| |data moved here
| |move occurs because `item` has type `T`, which does not implement the `Copy` trait
| help: consider removing the `&`: `item`
The key line in this error is cannot move out of type [T], a non-copy slice.
With our non-generic versions of the largest function, we were only trying to
find the largest i32 or char. As discussed in the “Stack-Only Data: Copy”
section in Chapter 4, types like i32 and char that have a known size can be
stored on the stack, so they implement the Copy trait. But when we made the
largest function generic, it became possible for the list parameter to have
types in it that don’t implement the Copy trait. Consequently, we wouldn’t be
able to move the value out of list[0] and into the largest variable,
resulting in this error.
To call this code with only those types that implement the Copy trait, we can
add Copy to the trait bounds of T! Listing 10-15 shows the complete code of
a generic largest function that will compile as long as the types of the
values in the slice that we pass into the function implement the PartialOrd
and Copy traits, like i32 and char do.
Filename: src/main.rs
fn largest<T: PartialOrd + Copy>(list: &[T]) -> T {
let mut largest = list[0];
for &item in list {
if item > largest {
largest = item;
}
}
largest
}
fn main() {
let number_list = vec![34, 50, 25, 100, 65];
let result = largest(&number_list);
println!("The largest number is {}", result);
let char_list = vec!['y', 'm', 'a', 'q'];
let result = largest(&char_list);
println!("The largest char is {}", result);
}
Listing 10-15: A working definition of the largest function that works on any
generic type that implements the PartialOrd and Copy traits
If we don’t want to restrict the largest function to the types that implement
the Copy trait, we could specify that T has the trait bound Clone instead
of Copy. Then we could clone each value in the slice when we want the
largest function to have ownership. Using the clone function means we’re
potentially making more heap allocations in the case of types that own heap
data like String, and heap allocations can be slow if we’re working with
large amounts of data.
We could also implement largest by having the function return a reference to
a T value in the slice. If we change the return type to &T instead of T,
thereby changing the body of the function to return a reference, we wouldn’t
need the Clone or Copy trait bounds and we could avoid heap allocations.
Try implementing these alternate solutions on your own! If you get stuck with
errors having to do with lifetimes, keep reading: the “Validating References
with Lifetimes” section coming up will explain, but lifetimes aren’t required
to solve these challenges.
By using a trait bound with an impl block that uses generic type parameters,
we can implement methods conditionally for types that implement the specified
traits. For example, the type Pair<T> in Listing 10-16 always implements the
new function to return a new instance of Pair<T> (recall from the ”Defining
Methods” section of Chapter 5 that Self is a type alias for the type of the
impl block, which in this case is Pair<T>). But in the next impl block,
Pair<T> only implements the cmp_display method if its inner type T
implements the PartialOrd trait that enables comparison and the Display
trait that enables printing.
Filename: src/lib.rs
use std::fmt::Display;
struct Pair<T> {
x: T,
y: T,
}
impl<T> Pair<T> {
fn new(x: T, y: T) -> Self {
Self { x, y }
}
}
impl<T: Display + PartialOrd> Pair<T> {
fn cmp_display(&self) {
if self.x >= self.y {
println!("The largest member is x = {}", self.x);
} else {
println!("The largest member is y = {}", self.y);
}
}
}
Listing 10-16: Conditionally implementing methods on a generic type depending on trait bounds
We can also conditionally implement a trait for any type that implements
another trait. Implementations of a trait on any type that satisfies the trait
bounds are called blanket implementations and are extensively used in the
Rust standard library. For example, the standard library implements the
ToString trait on any type that implements the Display trait. The impl
block in the standard library looks similar to this code:
impl<T: Display> ToString for T {
// --snip--
}
Because the standard library has this blanket implementation, we can call the
to_string method defined by the ToString trait on any type that implements
the Display trait. For example, we can turn integers into their corresponding
String values like this because integers implement Display:
let s = 3.to_string();
Blanket implementations appear in the documentation for the trait in the “Implementors” section.
Traits and trait bounds let us write code that uses generic type parameters to reduce duplication but also specify to the compiler that we want the generic type to have particular behavior. The compiler can then use the trait bound information to check that all the concrete types used with our code provide the correct behavior. In dynamically typed languages, we would get an error at runtime if we called a method on a type which didn’t define the method. But Rust moves these errors to compile time so we’re forced to fix the problems before our code is even able to run. Additionally, we don’t have to write code that checks for behavior at runtime because we’ve already checked at compile time. Doing so improves performance without having to give up the flexibility of generics.
Lifetimes are another kind of generic that we’ve already been using. Rather than ensuring that a type has the behavior we want, lifetimes ensure that references are valid as long as we need them to be.
One detail we didn’t discuss in the “References and Borrowing” section in Chapter 4 is that every reference in Rust has a lifetime, which is the scope for which that reference is valid. Most of the time, lifetimes are implicit and inferred, just like most of the time, types are inferred. We only must annotate types when multiple types are possible. In a similar way, we must annotate lifetimes when the lifetimes of references could be related in a few different ways. Rust requires us to annotate the relationships using generic lifetime parameters to ensure the actual references used at runtime will definitely be valid.
Annotating lifetimes is not even a concept most other programming languages have, so this is going to feel unfamiliar. Although we won’t cover lifetimes in their entirety in this chapter, we’ll discuss common ways you might encounter lifetime syntax so you can get comfortable with the concept.
The main aim of lifetimes is to prevent dangling references, which cause a program to reference data other than the data it’s intended to reference. Consider the program in Listing 10-17, which has an outer scope and an inner scope.
{
let r;
{
let x = 5;
r = &x;
}
println!("r: {}", r);
}
Listing 10-17: An attempt to use a reference whose value has gone out of scope
Note: The examples in Listings 10-17, 10-18, and 10-24 declare variables without giving them an initial value, so the variable name exists in the outer scope. At first glance, this might appear to be in conflict with Rust’s having no null values. However, if we try to use a variable before giving it a value, we’ll get a compile-time error, which shows that Rust indeed does not allow null values.
The outer scope declares a variable named r with no initial value, and the
inner scope declares a variable named x with the initial value of 5. Inside
the inner scope, we attempt to set the value of r as a reference to x. Then
the inner scope ends, and we attempt to print the value in r. This code won’t
compile because the value r is referring to has gone out of scope before we
try to use it. Here is the error message:
error[E0597]: `x` does not live long enough
--> src/main.rs:7:17
|
7 | r = &x;
| ^^ borrowed value does not live long enough
8 | }
| - `x` dropped here while still borrowed
9 |
10 | println!("r: {}", r);
| - borrow later used here
The variable x doesn’t “live long enough.” The reason is that x will be out
of scope when the inner scope ends on line 7. But r is still valid for the
outer scope; because its scope is larger, we say that it “lives longer.” If
Rust allowed this code to work, r would be referencing memory that was
deallocated when x went out of scope, and anything we tried to do with r
wouldn’t work correctly. So how does Rust determine that this code is invalid?
It uses a borrow checker.
The Rust compiler has a borrow checker that compares scopes to determine whether all borrows are valid. Listing 10-18 shows the same code as Listing 10-17 but with annotations showing the lifetimes of the variables.
{
let r; // ---------+-- 'a
// |
{ // |
let x = 5; // -+-- 'b |
r = &x; // | |
} // -+ |
// |
println!("r: {}", r); // |
} // ---------+
Listing 10-18: Annotations of the lifetimes of r and x, named 'a and
'b, respectively
Here, we’ve annotated the lifetime of r with 'a and the lifetime of x
with 'b. As you can see, the inner 'b block is much smaller than the outer
'a lifetime block. At compile time, Rust compares the size of the two
lifetimes and sees that r has a lifetime of 'a but that it refers to memory
with a lifetime of 'b. The program is rejected because 'b is shorter than
'a: the subject of the reference doesn’t live as long as the reference.
Listing 10-19 fixes the code so it doesn’t have a dangling reference and compiles without any errors.
{
let x = 5; // ----------+-- 'b
// |
let r = &x; // --+-- 'a |
// | |
println!("r: {}", r); // | |
// --+ |
} // ----------+
Listing 10-19: A valid reference because the data has a longer lifetime than the reference
Here, x has the lifetime 'b, which in this case is larger than 'a. This
means r can reference x because Rust knows that the reference in r will
always be valid while x is valid.
Now that you know where the lifetimes of references are and how Rust analyzes lifetimes to ensure references will always be valid, let’s explore generic lifetimes of parameters and return values in the context of functions.
We’ll write a function that returns the longer of two string slices. This
function will take two string slices and return a single string slice. After
we’ve implemented the longest function, the code in Listing 10-20 should
print The longest string is abcd.
Filename: src/main.rs
fn main() {
let string1 = String::from("abcd");
let string2 = "xyz";
let result = longest(string1.as_str(), string2);
println!("The longest string is {}", result);
}
Listing 10-20: A main function that calls the longest function to find the
longer of two string slices
Note that we want the function to take string slices, which are references,
rather than strings, because we don’t want the longest function to take
ownership of its parameters. Refer to the “String Slices as Parameters” section
in Chapter 4 for more discussion about why the parameters we use in Listing
10-20 are the ones we want.
If we try to implement the longest function as shown in Listing 10-21, it
won’t compile.
Filename: src/main.rs
fn longest(x: &str, y: &str) -> &str {
if x.len() > y.len() {
x
} else {
y
}
}
Listing 10-21: An implementation of the longest function that returns the
longer of two string slices but does not yet compile
Instead, we get the following error that talks about lifetimes:
error[E0106]: missing lifetime specifier
--> src/main.rs:9:33
|
9 | fn longest(x: &str, y: &str) -> &str {
| ---- ---- ^ expected named lifetime parameter
|
= help: this function's return type contains a borrowed value, but the signature does not say whether it is borrowed from `x` or `y`
help: consider introducing a named lifetime parameter
|
9 | fn longest<'a>(x: &'a str, y: &'a str) -> &'a str {
| ^^^^ ^^^^^^^ ^^^^^^^ ^^^
The help text reveals that the return type needs a generic lifetime parameter
on it because Rust can’t tell whether the reference being returned refers to
x or y. Actually, we don’t know either, because the if block in the body
of this function returns a reference to x and the else block returns a
reference to y!
When we’re defining this function, we don’t know the concrete values that will
be passed into this function, so we don’t know whether the if case or the
else case will execute. We also don’t know the concrete lifetimes of the
references that will be passed in, so we can’t look at the scopes as we did in
Listings 10-18 and 10-19 to determine whether the reference we return will
always be valid. The borrow checker can’t determine this either, because it
doesn’t know how the lifetimes of x and y relate to the lifetime of the
return value. To fix this error, we’ll add generic lifetime parameters that
define the relationship between the references so the borrow checker can
perform its analysis.
Lifetime annotations don’t change how long any of the references live. Rather, they describe the relationships of the lifetimes of multiple references to each other without affecting the lifetimes. Just as functions can accept any type when the signature specifies a generic type parameter, functions can accept references with any lifetime by specifying a generic lifetime parameter.
Lifetime annotations have a slightly unusual syntax: the names of lifetime
parameters must start with an apostrophe (') and are usually all lowercase
and very short, like generic types. Most people use the name 'a for the first
lifetime annotation. We place lifetime parameter annotations after the & of a
reference, using a space to separate the annotation from the reference’s type.
Here are some examples: a reference to an i32 without a lifetime parameter, a
reference to an i32 that has a lifetime parameter named 'a, and a mutable
reference to an i32 that also has the lifetime 'a.
&i32 // a reference
&'a i32 // a reference with an explicit lifetime
&'a mut i32 // a mutable reference with an explicit lifetime
One lifetime annotation by itself doesn’t have much meaning, because the
annotations are meant to tell Rust how generic lifetime parameters of multiple
references relate to each other. For example, let’s say we have a function with
the parameter first that is a reference to an i32 with lifetime 'a. The
function also has another parameter named second that is another reference to
an i32 that also has the lifetime 'a. The lifetime annotations indicate
that the references first and second must both live as long as that generic
lifetime.
Now let’s examine lifetime annotations in the context of the longest
function. As with generic type parameters, we need to declare generic lifetime
parameters inside angle brackets between the function name and the parameter
list. We want the signature to express the following constraint: the returned
reference will be valid as long as both the parameters are valid. This is the
relationship between lifetimes of the parameters and the return value. We’ll
name the lifetime 'a and then add it to each reference, as shown in Listing
10-22.
Filename: src/main.rs
fn longest<'a>(x: &'a str, y: &'a str) -> &'a str {
if x.len() > y.len() {
x
} else {
y
}
}
Listing 10-22: The longest function definition specifying that all the
references in the signature must have the same lifetime 'a
This code should compile and produce the result we want when we use it with the
main function in Listing 10-20.
The function signature now tells Rust that for some lifetime 'a, the function
takes two parameters, both of which are string slices that live at least as
long as lifetime 'a. The function signature also tells Rust that the string
slice returned from the function will live at least as long as lifetime 'a.
In practice, it means that the lifetime of the reference returned by the
longest function is the same as the smaller of the lifetimes of the
references passed in. These relationships are what we want Rust to use when
analyzing this code.
Remember, when we specify the lifetime parameters in this function signature,
we’re not changing the lifetimes of any values passed in or returned. Rather,
we’re specifying that the borrow checker should reject any values that don’t
adhere to these constraints. Note that the longest function doesn’t need to
know exactly how long x and y will live, only that some scope can be
substituted for 'a that will satisfy this signature.
When annotating lifetimes in functions, the annotations go in the function signature, not in the function body. The lifetime annotations become part of the contract of the function, much like the types in the signature. Having function signatures contain the lifetime contract means the analysis the Rust compiler does can be simpler. If there’s a problem with the way a function is annotated or the way it is called, the compiler errors can point to the part of our code and the constraints more precisely. If, instead, the Rust compiler made more inferences about what we intended the relationships of the lifetimes to be, the compiler might only be able to point to a use of our code many steps away from the cause of the problem.
When we pass concrete references to longest, the concrete lifetime that is
substituted for 'a is the part of the scope of x that overlaps with the
scope of y. In other words, the generic lifetime 'a will get the concrete
lifetime that is equal to the smaller of the lifetimes of x and y. Because
we’ve annotated the returned reference with the same lifetime parameter 'a,
the returned reference will also be valid for the length of the smaller of the
lifetimes of x and y.
Let’s look at how the lifetime annotations restrict the longest function by
passing in references that have different concrete lifetimes. Listing 10-23 is
a straightforward example.
Filename: src/main.rs
fn main() {
let string1 = String::from("long string is long");
{
let string2 = String::from("xyz");
let result = longest(string1.as_str(), string2.as_str());
println!("The longest string is {}", result);
}
}
Listing 10-23: Using the longest function with references to String values
that have different concrete lifetimes
In this example, string1 is valid until the end of the outer scope, string2
is valid until the end of the inner scope, and result references something
that is valid until the end of the inner scope. Run this code, and you’ll see
that the borrow checker approves; it will compile and print The longest string is long string is long.
Next, let’s try an example that shows that the lifetime of the reference in
result must be the smaller lifetime of the two arguments. We’ll move the
declaration of the result variable outside the inner scope but leave the
assignment of the value to the result variable inside the scope with
string2. Then we’ll move the println! that uses result to outside the
inner scope, after the inner scope has ended. The code in Listing 10-24 will
not compile.
Filename: src/main.rs
fn main() {
let string1 = String::from("long string is long");
let result;
{
let string2 = String::from("xyz");
result = longest(string1.as_str(), string2.as_str());
}
println!("The longest string is {}", result);
}
Listing 10-24: Attempting to use result after string2 has gone out of scope
When we try to compile this code, we get this error:
error[E0597]: `string2` does not live long enough
--> src/main.rs:6:44
|
6 | result = longest(string1.as_str(), string2.as_str());
| ^^^^^^^ borrowed value does not live long enough
7 | }
| - `string2` dropped here while still borrowed
8 | println!("The longest string is {}", result);
| ------ borrow later used here
The error shows that for result to be valid for the println! statement,
string2 would need to be valid until the end of the outer scope. Rust knows
this because we annotated the lifetimes of the function parameters and return
values using the same lifetime parameter 'a.
As humans, we can look at this code and see that string1 is longer than
string2 and therefore result will contain a reference to string1.
Because string1 has not gone out of scope yet, a reference to string1 will
still be valid for the println! statement. However, the compiler can’t see
that the reference is valid in this case. We’ve told Rust that the lifetime of
the reference returned by the longest function is the same as the smaller of
the lifetimes of the references passed in. Therefore, the borrow checker
disallows the code in Listing 10-24 as possibly having an invalid reference.
Try designing more experiments that vary the values and lifetimes of the
references passed in to the longest function and how the returned reference
is used. Make hypotheses about whether or not your experiments will pass the
borrow checker before you compile; then check to see if you’re right!
The way in which you need to specify lifetime parameters depends on what your
function is doing. For example, if we changed the implementation of the
longest function to always return the first parameter rather than the longest
string slice, we wouldn’t need to specify a lifetime on the y parameter. The
following code will compile:
Filename: src/main.rs
fn longest<'a>(x: &'a str, y: &str) -> &'a str {
x
}
We’ve specified a lifetime parameter 'a for the parameter x and the return
type, but not for the parameter y, because the lifetime of y does not have
any relationship with the lifetime of x or the return value.
When returning a reference from a function, the lifetime parameter for the
return type needs to match the lifetime parameter for one of the parameters. If
the reference returned does not refer to one of the parameters, it must refer
to a value created within this function. However, this would be a dangling
reference because the value will go out of scope at the end of the function.
Consider this attempted implementation of the longest function that won’t
compile:
Filename: src/main.rs
fn longest<'a>(x: &str, y: &str) -> &'a str {
let result = String::from("really long string");
result.as_str()
}
Here, even though we’ve specified a lifetime parameter 'a for the return
type, this implementation will fail to compile because the return value
lifetime is not related to the lifetime of the parameters at all. Here is the
error message we get:
error[E0515]: cannot return value referencing local variable `result`
--> src/main.rs:11:5
|
11 | result.as_str()
| ------^^^^^^^^^
| |
| returns a value referencing data owned by the current function
| `result` is borrowed here
The problem is that result goes out of scope and gets cleaned up at the end
of the longest function. We’re also trying to return a reference to result
from the function. There is no way we can specify lifetime parameters that
would change the dangling reference, and Rust won’t let us create a dangling
reference. In this case, the best fix would be to return an owned data type
rather than a reference so the calling function is then responsible for
cleaning up the value.
Ultimately, lifetime syntax is about connecting the lifetimes of various parameters and return values of functions. Once they’re connected, Rust has enough information to allow memory-safe operations and disallow operations that would create dangling pointers or otherwise violate memory safety.
So far, the structs we've define all hold owned types. We can define structs to
hold references, but in that case we would need to add a lifetime annotation on
every reference in the struct’s definition. Listing 10-25 has a struct named
ImportantExcerpt that holds a string slice.
Filename: src/main.rs
struct ImportantExcerpt<'a> {
part: &'a str,
}
fn main() {
let novel = String::from("Call me Ishmael. Some years ago...");
let first_sentence = novel.split('.').next().expect("Could not find a '.'");
let i = ImportantExcerpt {
part: first_sentence,
};
}
Listing 10-25: A struct that holds a reference, requiring a lifetime annotation
This struct has the single field part that holds a string slice, which is a
reference. As with generic data types, we declare the name of the generic
lifetime parameter inside angle brackets after the name of the struct so we can
use the lifetime parameter in the body of the struct definition. This
annotation means an instance of ImportantExcerpt can’t outlive the reference
it holds in its part field.
The main function here creates an instance of the ImportantExcerpt struct
that holds a reference to the first sentence of the String owned by the
variable novel. The data in novel exists before the ImportantExcerpt
instance is created. In addition, novel doesn’t go out of scope until after
the ImportantExcerpt goes out of scope, so the reference in the
ImportantExcerpt instance is valid.
You’ve learned that every reference has a lifetime and that you need to specify lifetime parameters for functions or structs that use references. However, in Chapter 4 we had a function in Listing 4-9, shown again in Listing 10-26, that compiled without lifetime annotations.
Filename: src/lib.rs
fn first_word(s: &str) -> &str {
let bytes = s.as_bytes();
for (i, &item) in bytes.iter().enumerate() {
if item == b' ' {
return &s[0..i];
}
}
&s[..]
}
Listing 10-26: A function we defined in Listing 4-9 that compiled without lifetime annotations, even though the parameter and return type are references
The reason this function compiles without lifetime annotations is historical: in early versions (pre-1.0) of Rust, this code wouldn’t have compiled because every reference needed an explicit lifetime. At that time, the function signature would have been written like this:
fn first_word<'a>(s: &'a str) -> &'a str {
After writing a lot of Rust code, the Rust team found that Rust programmers were entering the same lifetime annotations over and over in particular situations. These situations were predictable and followed a few deterministic patterns. The developers programmed these patterns into the compiler’s code so the borrow checker could infer the lifetimes in these situations and wouldn’t need explicit annotations.
This piece of Rust history is relevant because it’s possible that more deterministic patterns will emerge and be added to the compiler. In the future, even fewer lifetime annotations might be required.
The patterns programmed into Rust’s analysis of references are called the lifetime elision rules. These aren’t rules for programmers to follow; they’re a set of particular cases that the compiler will consider, and if your code fits these cases, you don’t need to write the lifetimes explicitly.
The elision rules don’t provide full inference. If Rust deterministically applies the rules but there is still ambiguity as to what lifetimes the references have, the compiler won’t guess what the lifetime of the remaining references should be. Instead of guessing, the compiler will give you an error that you can resolve by adding the lifetime annotations.
Lifetimes on function or method parameters are called input lifetimes, and lifetimes on return values are called output lifetimes.
The compiler uses three rules to figure out the lifetimes of the references
when there aren’t explicit annotations. The first rule applies to input
lifetimes, and the second and third rules apply to output lifetimes. If the
compiler gets to the end of the three rules and there are still references for
which it can’t figure out lifetimes, the compiler will stop with an error.
These rules apply to fn definitions as well as impl blocks.
The first rule is that the compiler assigns a lifetime parameter to each
paramter that’s a reference. In other words, a function with one parameter gets
one lifetime parameter: fn foo<'a>(x: &'a i32); a function with two
parameters gets two separate lifetime parameters: fn foo<'a, 'b>(x: &'a i32, y: &'b i32); and so on.
The second rule is that, if there is exactly one input lifetime parameter, that
lifetime is assigned to all output lifetime parameters: fn foo<'a>(x: &'a i32) -> &'a i32.
The third rule is that, if there are multiple input lifetime parameters, but
one of them is &self or &mut self because this is a method, the lifetime of
self is assigned to all output lifetime parameters. This third rule makes
methods much nicer to read and write because fewer symbols are necessary.
Let’s pretend we’re the compiler. We’ll apply these rules to figure out the
lifetimes of the references in the signature of the first_word function in
Listing 10-26. The signature starts without any lifetimes associated with the
references:
fn first_word(s: &str) -> &str {
Then the compiler applies the first rule, which specifies that each parameter
gets its own lifetime. We’ll call it 'a as usual, so now the signature is
this:
fn first_word<'a>(s: &'a str) -> &str {
The second rule applies because there is exactly one input lifetime. The second rule specifies that the lifetime of the one input parameter gets assigned to the output lifetime, so the signature is now this:
fn first_word<'a>(s: &'a str) -> &'a str {
Now all the references in this function signature have lifetimes, and the compiler can continue its analysis without needing the programmer to annotate the lifetimes in this function signature.
Let’s look at another example, this time using the longest function that had
no lifetime parameters when we started working with it in Listing 10-21:
fn longest(x: &str, y: &str) -> &str {
Let’s apply the first rule: each parameter gets its own lifetime. This time we have two parameters instead of one, so we have two lifetimes:
fn longest<'a, 'b>(x: &'a str, y: &'b str) -> &str {
You can see that the second rule doesn’t apply because there is more than one
input lifetime. The third rule doesn’t apply either, because longest is a
function rather than a method, so none of the parameters are self. After
working through all three rules, we still haven’t figured out what the return
type’s lifetime is. This is why we got an error trying to compile the code in
Listing 10-21: the compiler worked through the lifetime elision rules but still
couldn’t figure out all the lifetimes of the references in the signature.
Because the third rule really only applies in method signatures, we’ll look at lifetimes in that context next to see why the third rule means we don’t have to annotate lifetimes in method signatures very often.
When we implement methods on a struct with lifetimes, we use the same syntax as that of generic type parameters shown in Listing 10-11. Where we declare and use the lifetime parameters depends on whether they’re related to the struct fields or the method parameters and return values.
Lifetime names for struct fields always need to be declared after the impl
keyword and then used after the struct’s name, because those lifetimes are part
of the struct’s type.
In method signatures inside the impl block, references might be tied to the
lifetime of references in the struct’s fields, or they might be independent. In
addition, the lifetime elision rules often make it so that lifetime annotations
aren’t necessary in method signatures. Let’s look at some examples using the
struct named ImportantExcerpt that we defined in Listing 10-25.
First, we’ll use a method named level whose only parameter is a reference to
self and whose return value is an i32, which is not a reference to anything:
impl<'a> ImportantExcerpt<'a> {
fn level(&self) -> i32 {
3
}
}
The lifetime parameter declaration after impl and its use after the type name
are required, but we’re not required to annotate the lifetime of the reference
to self because of the first elision rule.
Here is an example where the third lifetime elision rule applies:
impl<'a> ImportantExcerpt<'a> {
fn announce_and_return_part(&self, announcement: &str) -> &str {
println!("Attention please: {}", announcement);
self.part
}
}
There are two input lifetimes, so Rust applies the first lifetime elision rule
and gives both &self and announcement their own lifetimes. Then, because
one of the parameters is &self, the return type gets the lifetime of &self,
and all lifetimes have been accounted for.
One special lifetime we need to discuss is 'static, which denotes that the
affected reference can live for the entire duration of the program. All
string literals have the 'static lifetime, which we can annotate as follows:
let s: &'static str = "I have a static lifetime.";
The text of this string is stored directly in the program’s binary, which
is always available. Therefore, the lifetime of all string literals is
'static.
You might see suggestions to use the 'static lifetime in error messages. But
before specifying 'static as the lifetime for a reference, think about
whether the reference you have actually lives the entire lifetime of your
program or not, and whether you want it to. Most of the time, an error message
suggesting the 'static lifetime results from attempting to create a
dangling reference or a mismatch of the available lifetimes. In such cases, the
solution is fixing those problems, not specifying the 'static lifetime.
Let’s briefly look at the syntax of specifying generic type parameters, trait bounds, and lifetimes all in one function!
use std::fmt::Display;
fn longest_with_an_announcement<'a, T>(
x: &'a str,
y: &'a str,
ann: T,
) -> &'a str
where
T: Display,
{
println!("Announcement! {}", ann);
if x.len() > y.len() {
x
} else {
y
}
}
This is the longest function from Listing 10-22 that returns the longer of
two string slices. But now it has an extra parameter named ann of the generic
type T, which can be filled in by any type that implements the Display
trait as specified by the where clause. This extra parameter will be printed
using {}, which is why the Display trait bound is necessary. Because
lifetimes are a type of generic, the declarations of the lifetime parameter
'a and the generic type parameter T go in the same list inside the angle
brackets after the function name.
We covered a lot in this chapter! Now that you know about generic type parameters, traits and trait bounds, and generic lifetime parameters, you’re ready to write code without repetition that works in many different situations. Generic type parameters let you apply the code to different types. Traits and trait bounds ensure that even though the types are generic, they’ll have the behavior the code needs. You learned how to use lifetime annotations to ensure that this flexible code won’t have any dangling references. And all of this analysis happens at compile time, which doesn’t affect runtime performance!
Believe it or not, there is much more to learn on the topics we discussed in this chapter: Chapter 17 discusses trait objects, which are another way to use traits. There are also more complex scenarios involving lifetime annotations that you will only need in very advanced scenarios; for those, you should read the Rust Reference at https://doc.rust-lang.org/reference/trait-bounds.html. But next, you’ll learn how to write tests in Rust so you can make sure your code is working the way it should.